Computational model: populations, connectivity and input.

<p>(A) Diagram of the attractor model for decision-making between up to four choice alternatives. The network consists of a population of excitatory pyramidal neurons, structured into four selective pools (red, each contains 20% of the excitatory neurons) and a nonselective population, that inhibit each other through shared feedback from an inhibitory pool of interneurons (orange). Unlabeled arrows denote a connectivity of 1 (baseline). Recurrent connectivity within a selective pool is high, ω₊ = 1.48, whereas the connection weight between the selective pools is below average ω₋ = 0.88. Inhibitory connections have a weight ω_I = 1.125. The network consists of 500 neurons. (B) Time course of target and motion input to the selective populations in order to model the experimental design of the RDM task (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043131#s2" target="_blank">methods</a>). (C) Example trial with “change of mind” for four alternatives at 3.2% coherence. The initially winning population (first threshold crossing) is overtaken by the other transient. The horizontal black line at 37 Hz indicates the threshold. Dotted vertical lines mark times of threshold crossings.